Analysis of Various Sentiment Analysis Techniques of NLP

Abstract

Sentiment Analysis and opinion mining complement each other. This approach is concerned with classifying the opinions of public regrading a product using NLP. This approach considers sentiments and viewpoint of individuals about the occurrence of an episode. Assessment mining is generally helpful in different domains such as business brand surveys, web-based media study, and movie analyses, and so forth. The sentiment analysis is an important method in formation of frameworks based on recommendations. The people give the content audits like web-based reviews, feedback or remarks on the web-based media and web-based business sites. This textual content is an essential wellspring of client's conclusions. In sentiment analysis, the main aim is to classify the users' sentiments into positive, negative and unbiased. This type of analysis indicates the ubiquity or importance the product in the marketplace. Every human being has their different opinion, feelings, thoughts, and emotion for an event this can be known with the help of sentiment analysis. The extraction of features and classification are the steps in sentiment analysis. This work is centered around implementing a fresh approach to sentiment analysis. The sentiment analysis techniques have various phases which include pre-processing, feature extraction and classification. The various machine learning algorithms for sentiment analysis are reviewed in terms of certain parameters.